Utilizing Support Vector Machines as a classification engine

Support Vector Machines (SVMs) are a family of extremely powerful models that can be used in classification and regression problems. In contrast to the preceding models, SVMs can handle highly nonlinear problems through a so-called kernel trick that implicitly maps the input vectors to higher-dimensional feature spaces. A broader explanation of SVMs can be found at http://www.statsoft.com/Textbook/Support-Vector-Machines.

Getting ready

To execute the following recipe, you will need Machine Learning PYthon (mlpy). The mlpy does not come with Anaconda so we need to install it manually. The mlpy requires GNU Scientific Library (GSL); on some systems, GSL might already be present, therefore, I recommend starting with installing mlpy first. Go to http://sourceforge.net/projects/mlpy/files/ and download the latest sources for mlpy (mlpy-<version>.tar.gz). Now, go to the command line and navigate to the folder you have downloaded...